Dataset Information

Peptide sequence confidence in accurate mass and time analysis and its use in complex proteomics experiments.

ABSTRACT: We present new algorithms and a software implementation for assigning confidence to peptide sequence assignments obtained through classic accurate mass and retention time (AMT) matching techniques, as well as methods for integrating these assignments with standard proteomics workflows. The algorithms are intended to increase the number of peptides and proteins identified (and, when applicable, quantitated by isotopic labeling) among related proteomics experiments that use high-resolution mass spectrometry instrumentation. The motivations for our extensions include the need to exploit high-resolution data to support highly complex proteomics experiments, especially those involving extensive off-line fractionation, to which recent label-free workflows might not easily generalize.

SUBMITTER: May D

PROVIDER: S-EPMC5831358 | biostudies-literature | 2008 Dec

REPOSITORIES: biostudies-literature

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Publications

Peptide sequence confidence in accurate mass and time analysis and its use in complex proteomics experiments.

May Damon D Liu Yan Y Law Wendy W Fitzgibbon Matt M Wang Hong H Hanash Samir S McIntosh Martin M

Journal of proteome research 20081201 12

We present new algorithms and a software implementation for assigning confidence to peptide sequence assignments obtained through classic accurate mass and retention time (AMT) matching techniques, as well as methods for integrating these assignments with standard proteomics workflows. The algorithms are intended to increase the number of peptides and proteins identified (and, when applicable, quantitated by isotopic labeling) among related proteomics experiments that use high-resolution mass sp ...[more]

PMID: 19367719

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Project description:BACKGROUND: Long-term space travel simulation experiments enabled to discover different aspects of human metabolism such as the complexity of NaCl salt balance. Detailed proteomics data were collected during the Mars105 isolation experiment enabling a deeper insight into the molecular processes involved. RESULTS: We studied the abundance of about two thousand proteins extracted from urine samples of six volunteers collected weekly during a 105-day isolation experiment under controlled dietary conditions including progressive reduction of salt consumption. Machine learning using Self Organizing maps (SOM) in combination with different analysis tools was applied to describe the time trajectories of protein abundance in urine. The method enables a personalized and intuitive view on the physiological state of the volunteers. The abundance of more than one half of the proteins measured clearly changes in the course of the experiment. The trajectory splits roughly into three time ranges, an early (week 1-6), an intermediate (week 7-11) and a late one (week 12-15). Regulatory modes associated with distinct biological processes were identified using previous knowledge by applying enrichment and pathway flow analysis. Early protein activation modes can be related to immune response and inflammatory processes, activation at intermediate times to developmental and proliferative processes and late activations to stress and responses to chemicals. CONCLUSIONS: The protein abundance profiles support previous results about alternative mechanisms of salt storage in an osmotically inactive form. We hypothesize that reduced NaCl consumption of about 6 g/day presumably will reduce or even prevent the activation of inflammatory processes observed in the early time range of isolation. SOM machine learning in combination with analysis methods of class discovery and functional annotation enable the straightforward analysis of complex proteomics data sets generated by means of mass spectrometry.

Dataset Information

Peptide sequence confidence in accurate mass and time analysis and its use in complex proteomics experiments.

Publications

Peptide sequence confidence in accurate mass and time analysis and its use in complex proteomics experiments.

Similar Datasets

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